The overall goal of this project is to determine the pattern of longitudinal structural, perfusion, and metabolic changes in the brain, which best predict future cognitive decline and dementia due to Alzheimer's disease (AD). Specifically we will determine the predictive "value added" of MRI/MRS to cognitive testing. We will perform longitudinal clinical evaluation, cognitive testing, and MRI/MRS studies on 130 completed non-demented subjects > 65 yr, presenting with memory complaints. The independent variables will be obtained from MRI/MRS at 0, 6, 12,and 24 mo. to measure brain structure, perfusion, and metabolism. Dependent variables will be decline of memory and executive function scores, assessed annually. Clinical dementia rating scale and conversion to dementia will be used as secondary dependent variables. The overall hypothesis is that MRI/MRSI predicts cognitive decline/dementia. Hypotheses: 1.) Initial entorhinal cortex (ERC) volume predicts cognitive decline/dementia and adds to the predictive value of baseline memory function. 2.) In addition to baseline memory function and ERC volume, rate of ERC atrophy improves prediction of cognitive decline. 3.) The best MRI predictors for memory decline are volume change over time of ERC and hippocampus, and these add to the predictive value of baseline memory function. The best predictors of executive function decline are volume changes in frontal gray matter and these add to the predictive value of baseline cognitive function. 4.) In addition to baseline memory function and changes of ERC volume, perfusion and metabolite (NAA, mI) changes over time in hippocampus temporal and parietal lobe further improve prediction of cognitive decline. In addition to the above hypotheses, we will explore what combination of cognitive, genetic (APOE) and MRI/MRS measures, and what between-scan interval, best predicts subsequent cognitive decline/dementia. This project is expected to improve methods for predicting cognitive decline/dementia due to AD; these techniques should identify those subjects at high risk for AD, who are candidates for primary prevention.